Video deblurring methods remove blurs by taking advantage of sharper scene patches from neighboring frames [1]}, [2]}.
However, since neighboring frames are usually not spatially aligned with the reference frame due to motions, it is non-trivial to construct visual correspondence between frames.
Existing works mainly tackle the misalignment between frames using homography or optical flows.
Some previous works [3]}, [4]}, [5]}, [6]}, [7]}, [8]} first estimate the relative motions using optical flows, then warp neighboring frames to the reference frame.
However, in occurrence of fast object motions with large displacements, the constraint on the velocity smoothness may not always hold. As a result, estimating optical flow from blurry images remains a challenging research problem by itself [9]}, [10]}.
In addition, these methods are also notoriously less effective in the presence of occlusions and severe depth variations.
Recent works [11]}, [12]} use dynamic filters to restore videos in the feature domain. However, their ability to address large pixel displacement is restricted by the local receptive fields.
Therefore, it remains a challenge to design a flexible yet effective visual correspondence method with abrupt motions.
